US20080284582A1 - System and method of discovering, detecting and classifying alarm patterns for electrophysiological monitoring systems - Google Patents
System and method of discovering, detecting and classifying alarm patterns for electrophysiological monitoring systems Download PDFInfo
- Publication number
- US20080284582A1 US20080284582A1 US11/749,400 US74940007A US2008284582A1 US 20080284582 A1 US20080284582 A1 US 20080284582A1 US 74940007 A US74940007 A US 74940007A US 2008284582 A1 US2008284582 A1 US 2008284582A1
- Authority
- US
- United States
- Prior art keywords
- alarm
- monitoring
- sequence
- pattern
- patterns
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 97
- 238000000034 method Methods 0.000 title claims abstract description 44
- 230000004044 response Effects 0.000 claims abstract description 16
- 230000036541 health Effects 0.000 claims abstract description 11
- 238000012806 monitoring device Methods 0.000 claims abstract description 10
- 238000012545 processing Methods 0.000 claims abstract description 9
- 239000013598 vector Substances 0.000 claims description 32
- 238000004422 calculation algorithm Methods 0.000 claims description 19
- 238000012706 support-vector machine Methods 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 5
- 238000007635 classification algorithm Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000003064 k means clustering Methods 0.000 claims description 2
- 229920003266 Leaf® Polymers 0.000 description 15
- 238000001514 detection method Methods 0.000 description 9
- 201000010099 disease Diseases 0.000 description 5
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 2
- 230000036772 blood pressure Effects 0.000 description 2
- 229910052760 oxygen Inorganic materials 0.000 description 2
- 239000001301 oxygen Substances 0.000 description 2
- 241000282326 Felis catus Species 0.000 description 1
- 208000010496 Heart Arrest Diseases 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- 208000001871 Tachycardia Diseases 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 230000000747 cardiac effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000003862 health status Effects 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000037361 pathway Effects 0.000 description 1
- 230000035790 physiological processes and functions Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 230000033764 rhythmic process Effects 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
- 230000006794 tachycardia Effects 0.000 description 1
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/0245—Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
- A61B5/02455—Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals provided with high/low alarm devices
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
- G16Z99/00—Subject matter not provided for in other main groups of this subclass
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H70/00—ICT specially adapted for the handling or processing of medical references
- G16H70/60—ICT specially adapted for the handling or processing of medical references relating to pathologies
Definitions
- the present invention relates generally to discovering, classifying and detecting alarm patterns and more particularly, to a method of discovering, classifying and detecting alarm patterns for electrophysiological monitoring systems.
- Electrophysiological monitoring systems may use an electrophysiological monitoring system to simultaneously monitor multiple health parameters such as blood pressure, heart rhythm, heart rate, and specific oxygen to determine a health condition of a patient.
- electrophysiological monitoring systems raise alarms when a monitored signal value crosses a threshold. Alarms may also be raised when a specific waveform or waveform property is detected in a short segment of a recorded signal, e.g., a moving window average. For example, if a patient's heart rate exceeds a certain level or threshold, an alarm may be recognized and generated.
- too many alarms may be generated to be of medical significance. That is, one alarm for a particular patient condition may be insignificant on its own. However, when the alarm is found in a sequence or group of alarms, it may indicate a particular patient health condition. Additionally, when insignificant alarms are generated, medical staff time is frivolously utilized in investigating such alarms, and when too many insignificant alarms are generated, medical staff may begin to ignore or to place a low priority in such alarms. When this occurs, a valid alarm may be ignored or treated with less urgency during a critical period, thus endangering the patient. Furthermore, additional non-critical alarms may be recognized due to faulty sensors, equipment malfunctions, or patient movement. These “non-actionable alarms” divert resources of medical personnel to non-critical alarms and reduce the efficiency of the monitoring process.
- known alarm pattern detection methods are based on time series signal processing methods that may fail to discover and/or recognize certain alarm patterns that are extended over a long period of time.
- alarm patterns may not be properly detected if they are interrupted by another non-critical alarm also referred to as an ‘interdigitated alarm.’
- known alarm pattern detection methods may only detect a critical medical condition after the entire alarm sequence is completed.
- an alarm pattern discovery, detection, and classification method that reduces the number of non-critical alarms, discovers alarm patterns from multiple concurrent and sequential alarm signals over an extended period of time, and can classify an alarm sequence with a medical condition before the alarm sequence is complete.
- the present invention is a directed method for discovery, classification and detection of alarm patterns for electrophysiological monitoring systems that overcomes the aforementioned drawbacks.
- a suffix substring data structure is used to discover alarm patterns in an alarm sequence. More specifically, discovery of alarm patterns from monitoring multiple alarm signals, efficiently detecting patterns in real-time, and the ability to associate physiological alarm pattern data with a health status of a patient in response to alarm type incident rates.
- an electrophysiological monitoring system including a plurality of sensors configured to detect one or more health parameters of a patient and a monitoring device configured to receive a plurality of sensing signals from the sensors and output a monitoring signal representative of an alarm sequence, wherein the alarm sequence comprises a set of alarm events identified in the sensing signals.
- the system also includes an on-line monitoring module configured to generate a suffix tree data structure in response to the monitoring signal to identify alarm patterns from the set of alarm events and classify the alarm sequence in response to the occurrences of alarm patterns in the alarm sequence.
- the on-line monitoring module is further configured to alert monitoring personnel of an alarm condition after processing the alarm sequence in real-time.
- a method for electrophysiological monitoring includes receiving a monitoring signal derived from a patient wherein the monitoring signal provides an alarm sequence, developing a suffix substring data structure to identify a plurality of alarm patterns from the alarm sequence, and generating an incidence vector that determines a relative incidence of each type of alarm pattern with respect to the plurality of alarm patterns in the alarm sequence.
- the method further includes grouping the incidence vector in a cluster using a clustering algorithm, classifying one or more patients according to their pattern incident rates which are obtained by extraction of alarm patterns and storing the classification of the incidence vector in a database.
- a method for electrophysiological monitoring includes receiving a monitoring signal from a patient wherein the monitoring signal provides an alarm sequence, wherein the alarm sequence includes at least one alarm pattern.
- the method also includes developing a suffix substring data structure in real-time to identify alarm patterns of medical interest contained in the alarm sequence and generating an incidence vector in response to the suffix substring data structure.
- the alarm sequence is classified to a classification cluster using a classification algorithm based on the incidence vector and monitoring personnel is alerted to indicate a condition corresponding to the cluster after processing the alarm sequence in real-time.
- FIG. 1 is an example functional block diagram of a patient monitoring system according to an embodiment of the present invention.
- FIG. 2 is an example flowchart of an off-line method for detecting and classifying patient alarm patterns according to another embodiment of the present invention.
- FIG. 3 is an example flowchart of an on-line method for detecting and classifying patient alarm patterns in real-time according to another embodiment of the present invention.
- FIG. 4 is an example flowchart of a real-time method for alarm pattern detection and alerting monitoring personnel according to an embodiment of the present invention.
- FIG. 5 is an example diagram illustrating a suffix tree data structure according to an embodiment of the present invention.
- FIG. 6 is an example diagram of a plurality of incidence vectors contained in a pattern database, according to an embodiment of the present invention.
- references throughout this specification to “one embodiment,” “an embodiment,” “one example,” or “an example” mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention.
- the appearances of the phrases “in one embodiment,” “in an embodiment,” “in one example,” or “in an example” in various places throughout this specification are not necessarily all referring to the same embodiment.
- the particular features, structures or characteristics may be combined for example into any suitable combinations and/or sub-combinations in one ore more embodiments or examples.
- the particular features, structures, or characteristics may be included in an integrated circuit, an electronic circuit, a process (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, or other suitable components that provide the described functionality.
- FIG. 1 shows a functional block diagram of a monitoring system 100 .
- System 100 includes a monitoring device 102 , a monitoring station 104 , a remote storage facility 106 , a plurality of sensors 108 , and a real-time pattern display 111 .
- the monitoring device 102 receives a plurality of sensing signals 110 from sensor 108 .
- the monitoring device 102 may be APEX PROTM by GE Healthcare.
- the monitoring device 102 outputs a waveform monitoring signal 114 in response to the sensing signals 110 .
- sensors 108 are coupled to patient 112 to monitor and detect health parameters such as, heart rate, heart beat, blood pressure, specific oxygen, blood sugar, or the like.
- the patient 112 may be a human or other animal such as, a cat or a dog.
- the sensors 108 or monitoring device 102 may be wireless sensors that transmit sensing signals 110 through a wireless network.
- the monitoring signal 114 is received by monitoring station 104 in order to detect alarms and classify alarm patterns and store them in an alarm pattern database 116 .
- Database 116 may incorporate a chronological alarm log file containing all alarm times and types during one or more monitored sessions.
- Monitoring station 104 includes an on-line monitoring module 119 and an on-line learning module 120 .
- the on-line monitoring module 119 monitors a patient's physiological status and alerts monitoring personnel when an alarm pattern of significance has been detected via a real-time pattern display 111 that is used by monitoring personnel to view alarm sequence of the patient 112 .
- the real-time pattern display 111 may be included in monitoring device 102 or in monitoring station 104 .
- monitoring station 104 alerts a user, such as a physician, nurse, or other qualified medical personnel when an alarm or alarm pattern from database 116 is recognized in the monitoring signal 114 . The alert may be displayed to the user on the real-time pattern display 111 .
- monitoring station 104 or real-time pattern display 111 may generate an audible alert. Monitoring station 104 , with real-time display 111 may also generate a table or graph showing recorded alarm patterns of interest for the patient 112 for a user on real-time pattern display 111 . In this manner, a physician or other medical practitioner may review patient 112 for a particular period.
- the monitoring signal 114 may be multiple signals. In another embodiment, the monitoring station 104 may be coupled to receive a monitoring signal 114 from multiple patients. In one embodiment, the monitoring station 104 may be located in a hospital, clinic, or other medical facility and/or location where monitoring personnel may monitor patient 112 such as a monitoring facility.
- the remote storage facility 106 is connected to the monitoring station 104 through a communications link 121 and is configured to communicate with, receive, and store the detected alarm patterns identified by the monitoring station 104 . More specifically, the remote storage facility 106 includes alarm pattern database 116 and an alarm pattern library 117 , which may be accessed in real-time to compare alarm patterns in the monitoring signal 114 with stored alarm patterns in alarm pattern library 117 . Additionally, the monitoring station 104 includes an off-line learning module 118 that recognizes and stores alarm sequences while processing electrical medical records to further increase and/or diagnose more physiological states.
- FIG. 2 is a flowchart 200 of an off-line method for discovering and classifying patient alarm patterns according to an embodiment of the present invention.
- the flowchart 200 may be implemented in off-line learning module 118 of FIG. 1 .
- a pattern discovery algorithm is implemented by generating a suffix tree data structure to find alarm patterns from multiple patients. More specifically, the suffix tree data structure is used in the pattern discovery algorithm to identify and store new alarm patterns in alarm sequences, in which many alarm patterns may go undetected using traditional alarm pattern detection methods.
- FIG. 5 illustrates an example suffix tree data structure 500 according to the teachings of the present invention.
- the suffix tree data structure 500 several alarm types 502 of interest are considered for detecting alarm patterns in an alarm sequence. More specifically the alarm types 502 considered are, a low heart rate event that is represented by a letter ‘L’, an asystole event that is represented by the letter ‘A’, and a tachycardia event that is represented by the symbol ‘T.’
- the suffix tree data structure 500 includes a central node 504 , a plurality of first stems 508 , 510 , and 512 , a plurality of first leaves 516 , 518 , and 520 , a plurality of second stems 522 , 524 , 526 , and 528 , and a plurality of second leaves 530 , 532 , 534 , and 536 .
- Each first leaves 516 , 518 and 520 and each second leaves 530 , 532 , 534 , and 536 are associated with a specific alarm pattern.
- first leaf 508 is generated when an alarm pattern ‘TL’ is first recognized from an alarm sequence.
- second leaf 532 is generated when an alarm pattern ‘TLTLA’ is first recognized.
- a stem also referred to as a pathway, to a new leaf specific to that alarm pattern.
- a stem will be generated from that leaf to another leaf.
- an alarm pattern ‘TL’ represented by leaf 516 is a substring that is common to the alarm pattern ‘TLTLA’ represented by second leaf 536 . Since ‘TL’ is common to the alarm pattern TLA, second stem 528 is generated from first leaf 516 to generate second leaf 536 .
- the number of occurrences for the alarm pattern type corresponding to a patient history will be accounted for with an incidence vector, which is discussed in further detail below.
- the repeated occurrences of alarm patterns may be accounted for by weighting the nodes of the suffix tree data structure 500 . For example, if pattern type ‘LTLA’ occurs five times then second leaf 532 and pattern type ‘LA’ occurs three times, then first leaf 518 that is representative of ‘L’ will be weighted more than second leaf 532 and 530 .
- first leaf 518 is weighted value equal to the sum of the weighted value of second leafs 532 and 530 .
- a longest common substring may be detected with a suffix tree data structure 500 in order to identify common alarm patterns. More specifically, a longest common substring is defined as the longest string that is a substring of two or more strings. For example, the longest common substring in suffix tree data structure 500 is ‘TL’ which corresponds with first stem 508 and first node 516 . In one embodiment, more than one longest common substring may be determined in a suffix tree data structure.
- a least common substring is determined to determine the alarm pattern or patterns that are the shortest that are common to all patients in a class (i.e., disease category or medical condition). In one example, this may be used to distinguish a rare medical event and/or condition.
- a longest common subsequence is determined. More specifically, a longest common subsequence is defined as a longest sequence such as a subsequence of all sequences in a set of sequences. In one example, the longest common subsequence may be set to a maximum and/or a minimum length that is to be identified.
- the alarm patterns are screened for relevant alarms. More specifically, the alarm patterns may be screened, but not limited to, the longest common substring, the least common substring and the longest common subsequence. This allows certain alarm events that may be irrelevant such as sensor failures, patient movement, or independent alarm events that have no significance, to be excluded from the alarm patterns of interest.
- each incidence vector determines the relative frequency of occurrence of an alarm pattern type with respect to all the alarm pattern type that occur in the alarm sequence of a particular patient or patient class.
- a series of incidence vectors 602 may be calculated from occurrence values 604 in alarm pattern library 116 .
- Each occurrence value 604 is representative of a ratio of the number of occurrences of an alarm pattern ‘m’ over the total number of alarm pattern occurrences for patient ‘n,’ where ‘n’ is representative of a patient and ‘m’ is representative of an alarm pattern or alarm pattern type.
- occurrence value 606 is representative of a ratio of the number of occurrences of an alarm pattern ‘2’ over the total number of alarm pattern occurrences for patient ‘1’.
- each row 608 of occurrence values 604 corresponds to a respective incidence vector 602 and each column 610 of occurrence values 604 corresponds to a specific alarm pattern. Multiple occurrence values 604 make up the incidence vector 602 .
- Each incidence vector 602 establishes a relative incidence of alarm pattern type with respect to the corresponding alarm sequence during an entire patient monitoring session.
- multiple incidence vectors 602 are determined.
- flow chart 200 may be implemented in off-line learning module 118 .
- a clustering algorithm is used to group incidence vectors representative of alarm sequences together. More specifically, clustering is the classification of objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters) such that the data in each subset share some common trait (in this case common pattern incidence rates). Clustering is beneficial for scalability, defining arbitrary boundaries of a group, ability to deal with noise or outliers, insensitivity to order of input, and high dimensionality (number of patients).
- a K-means clustering algorithm is used which is known to one skilled in the art.
- other clustering algorithms such as, but not limited to, hierarchical, Fuzzy-C means, or “mixture-of-Gaussians” clustering algorithms may be used.
- designing a classifier for clusters of incidence vectors is accomplished using a support vector machine (SVM) algorithm. More specifically, the SVM algorithm will associate alarm pattern statistics with, a particular cluster associated with disease, type of medical condition, and/or general state of health, which is a method of unsupervised learning.
- the International Classification of Diseases (ICD) includes a taxonomy of cardiac and cardiac-relate diseases and may be used to classify existing electric medical records.
- a supervised learning process may classify the incidence vectors. For example, once the clusters have been developed, one or more qualified medical personnel, such as a physician(s), may asses the incidence vectors of alarm sequences to classify each cluster.
- One benefit of using the SVM algorithm is to efficiently classify non-normally distributed clusters with multi-dimensional data, the dimensions being the number of patterns that are to be classified.
- FIG. 3 is an example flowchart 300 of an on-line method for detecting and classifying new alarm patterns in real-time according to another embodiment of the present invention.
- flow chart 300 may be implemented in on-line learning module 120 and off-line learning module 118 .
- a suffix tree data structure is developed in real-time as alarm patterns are detected in an alarm sequence.
- the suffix tree data structure is compared with an alarm pattern library 117 using a comparing algorithm to detect new alarm patterns. If a new alarm pattern is detected 332 in block 330 , then the new alarm pattern type will be updated at block 340 into the alarm pattern library 117 .
- block 345 the off-line learning process will run when a substantial amount of new alarm patterns have been recognized to update the suffix tree data structure.
- block 345 may be executed after every new alarm pattern type is detected.
- block 345 may be executed after a threshold number of new alarm pattern types have been detected and stored in alarm pattern library 117 . If a new alarm pattern is not detected 346 in block 330 or after the alarm pattern library 117 is updated in block 340 , the incidence vector will be classified if patient's medical status or other alarm-related condition is known in block 350 . More specifically, the medical status may include types of diseases, medical status of the patient, or the like. If a patient's pattern incidence vector is not recognized, it is stored for future off-line processing, and the patient is classified as “other”, i.e., not similar to any previously known alarm patterns.
- FIG. 4 is a flowchart 400 of a method for real-time alarm pattern detection and alerting of monitoring personnel according to an embodiment of the present invention.
- flow chart 400 may be implemented in on-line monitoring module 119 .
- an alarm sequence is received.
- alarm sequences may be received concurrently from multiple individual monitoring signals 114 of a patient.
- the monitoring signal 114 may be an interdigitated alarm signal that represents alarm events of different types and possibly for multiple physiological parameters.
- a suffix tree data structure is developed in real-time to identify alarm patterns.
- an incidence vector is built up in real-time in response to the suffix tree data structure.
- a support vector machine algorithm is used to group the incidence vector with a cluster when sufficient alarm sequence data has been accumulated.
- an alert is raised to monitoring personnel to indicate a particular classification of the patient.
- certain critical alarm patterns are detected early in the alarm sequence, it may be possible to classify the alarm sequence with a particular “critical” cluster/group before the end of the alarm sequence.
- certain critical alarm patterns are discovered in an alarm sequence, it may be possible to eliminate group/clusters that are classified with non-significant phenomena, such as, ‘normal behavior,’ ‘stable,’ and/or ‘recovering.’
- Early classification before the alarm sequence is complete may provide prognostic value in critical conditions.
- a technical contribution for the disclosed method and apparatus is that is provides for a computer implemented method for discovery, classification and detection of alarm patterns for electrophysiological monitoring systems.
- an electrophysiological monitoring system including a plurality of sensors configured to detect one or more health parameters of a patient and a monitoring device configured to receive a plurality of sensing signals from the sensors and output a monitoring signal representative of an alarm sequence, wherein the alarm sequence comprises a set of alarm events identified in the sensing signals.
- the system also includes an on-line monitoring module configured to generate a suffix tree data structure in response to the monitoring signal to identify alarm patterns from the set of alarm events and classify the alarm sequence in response to the occurrences of alarm patterns in the alarm sequence.
- the on-line monitoring module is further configured to alert monitoring personnel of an alarm condition after processing the alarm sequence in real-time.
- a method for electrophysiological monitoring includes receiving a monitoring signal derived from a patient wherein the monitoring signal provides an alarm sequence, developing a suffix substring data structure to identify a plurality of alarm patterns from the alarm sequence, and generating an incidence vector that determines a relative incidence of each type of alarm pattern with respect to the plurality of alarm patterns in the alarm sequence.
- the method further includes grouping the incidence vector in a cluster using a clustering algorithm, classifying one or more patients according to their pattern incident rates which are obtained by extraction of alarm patterns and storing the classification of the incidence vector in a database.
- a method for electrophysiological monitoring includes receiving a monitoring signal from a patient wherein the monitoring signal provides an alarm sequence, wherein the alarm sequence includes at least one alarm pattern.
- the method also includes developing a suffix substring data structure in real-time to identify alarm patterns of medical interest contained in the alarm sequence and generating an incidence vector in response to the suffix substring data structure.
- the alarm sequence is classified to a classification cluster using a classification algorithm based on the incidence vector and monitoring personnel is alerted to indicate a condition corresponding to the cluster after processing the alarm sequence in real-time.
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Public Health (AREA)
- Medical Informatics (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Physiology (AREA)
- Pathology (AREA)
- Cardiology (AREA)
- Heart & Thoracic Surgery (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- Biophysics (AREA)
- Veterinary Medicine (AREA)
- Artificial Intelligence (AREA)
- Signal Processing (AREA)
- Pulmonology (AREA)
- Mathematical Physics (AREA)
- Psychiatry (AREA)
- Emergency Management (AREA)
- Evolutionary Computation (AREA)
- Fuzzy Systems (AREA)
- Business, Economics & Management (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Physics & Mathematics (AREA)
- Gerontology & Geriatric Medicine (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
Description
- The present invention relates generally to discovering, classifying and detecting alarm patterns and more particularly, to a method of discovering, classifying and detecting alarm patterns for electrophysiological monitoring systems.
- Monitoring personnel, such as a physician or nurse, may use an electrophysiological monitoring system to simultaneously monitor multiple health parameters such as blood pressure, heart rhythm, heart rate, and specific oxygen to determine a health condition of a patient. Typically, electrophysiological monitoring systems raise alarms when a monitored signal value crosses a threshold. Alarms may also be raised when a specific waveform or waveform property is detected in a short segment of a recorded signal, e.g., a moving window average. For example, if a patient's heart rate exceeds a certain level or threshold, an alarm may be recognized and generated.
- With such traditional detection methods, too many alarms may be generated to be of medical significance. That is, one alarm for a particular patient condition may be insignificant on its own. However, when the alarm is found in a sequence or group of alarms, it may indicate a particular patient health condition. Additionally, when insignificant alarms are generated, medical staff time is frivolously utilized in investigating such alarms, and when too many insignificant alarms are generated, medical staff may begin to ignore or to place a low priority in such alarms. When this occurs, a valid alarm may be ignored or treated with less urgency during a critical period, thus endangering the patient. Furthermore, additional non-critical alarms may be recognized due to faulty sensors, equipment malfunctions, or patient movement. These “non-actionable alarms” divert resources of medical personnel to non-critical alarms and reduce the efficiency of the monitoring process.
- Therefore it would be beneficial to discover and detect alarm patterns in an alarm sequence to identify critical health conditions in order to reduce the number of non-critical alarms. However, known alarm pattern detection methods are based on time series signal processing methods that may fail to discover and/or recognize certain alarm patterns that are extended over a long period of time. In addition, alarm patterns may not be properly detected if they are interrupted by another non-critical alarm also referred to as an ‘interdigitated alarm.’ Furthermore, known alarm pattern detection methods may only detect a critical medical condition after the entire alarm sequence is completed.
- Therefore, it would be beneficial to design an alarm pattern discovery, detection, and classification method that reduces the number of non-critical alarms, discovers alarm patterns from multiple concurrent and sequential alarm signals over an extended period of time, and can classify an alarm sequence with a medical condition before the alarm sequence is complete.
- The present invention is a directed method for discovery, classification and detection of alarm patterns for electrophysiological monitoring systems that overcomes the aforementioned drawbacks. A suffix substring data structure is used to discover alarm patterns in an alarm sequence. More specifically, discovery of alarm patterns from monitoring multiple alarm signals, efficiently detecting patterns in real-time, and the ability to associate physiological alarm pattern data with a health status of a patient in response to alarm type incident rates.
- According to an aspect of the present invention, an electrophysiological monitoring system including a plurality of sensors configured to detect one or more health parameters of a patient and a monitoring device configured to receive a plurality of sensing signals from the sensors and output a monitoring signal representative of an alarm sequence, wherein the alarm sequence comprises a set of alarm events identified in the sensing signals. The system also includes an on-line monitoring module configured to generate a suffix tree data structure in response to the monitoring signal to identify alarm patterns from the set of alarm events and classify the alarm sequence in response to the occurrences of alarm patterns in the alarm sequence. The on-line monitoring module is further configured to alert monitoring personnel of an alarm condition after processing the alarm sequence in real-time.
- According to another aspect of the present invention, a method for electrophysiological monitoring includes receiving a monitoring signal derived from a patient wherein the monitoring signal provides an alarm sequence, developing a suffix substring data structure to identify a plurality of alarm patterns from the alarm sequence, and generating an incidence vector that determines a relative incidence of each type of alarm pattern with respect to the plurality of alarm patterns in the alarm sequence. The method further includes grouping the incidence vector in a cluster using a clustering algorithm, classifying one or more patients according to their pattern incident rates which are obtained by extraction of alarm patterns and storing the classification of the incidence vector in a database.
- According to yet another aspect of the present invention, a method for electrophysiological monitoring includes receiving a monitoring signal from a patient wherein the monitoring signal provides an alarm sequence, wherein the alarm sequence includes at least one alarm pattern. The method also includes developing a suffix substring data structure in real-time to identify alarm patterns of medical interest contained in the alarm sequence and generating an incidence vector in response to the suffix substring data structure. The alarm sequence is classified to a classification cluster using a classification algorithm based on the incidence vector and monitoring personnel is alerted to indicate a condition corresponding to the cluster after processing the alarm sequence in real-time.
- Various other features and advantages of the present invention will be made apparent from the following detailed description and the drawings.
- The drawings illustrate one preferred embodiment presently contemplated for carrying out the invention.
-
FIG. 1 is an example functional block diagram of a patient monitoring system according to an embodiment of the present invention. -
FIG. 2 is an example flowchart of an off-line method for detecting and classifying patient alarm patterns according to another embodiment of the present invention. -
FIG. 3 is an example flowchart of an on-line method for detecting and classifying patient alarm patterns in real-time according to another embodiment of the present invention. -
FIG. 4 is an example flowchart of a real-time method for alarm pattern detection and alerting monitoring personnel according to an embodiment of the present invention. -
FIG. 5 is an example diagram illustrating a suffix tree data structure according to an embodiment of the present invention. -
FIG. 6 is an example diagram of a plurality of incidence vectors contained in a pattern database, according to an embodiment of the present invention. - References throughout this specification to “one embodiment,” “an embodiment,” “one example,” or “an example” mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases “in one embodiment,” “in an embodiment,” “in one example,” or “in an example” in various places throughout this specification are not necessarily all referring to the same embodiment. The particular features, structures or characteristics may be combined for example into any suitable combinations and/or sub-combinations in one ore more embodiments or examples. Furthermore, the particular features, structures, or characteristics may be included in an integrated circuit, an electronic circuit, a process (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, or other suitable components that provide the described functionality.
- In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one having ordinary skill in the art that the specific detail need not be employed to practice the present invention. In other instances, well-known materials or methods have not been described in detail in order to avoid obscuring the present invention.
-
FIG. 1 shows a functional block diagram of amonitoring system 100.System 100 includes amonitoring device 102, amonitoring station 104, aremote storage facility 106, a plurality ofsensors 108, and a real-time pattern display 111. Themonitoring device 102 receives a plurality ofsensing signals 110 fromsensor 108. In one embodiment, themonitoring device 102 may be APEX PRO™ by GE Healthcare. Themonitoring device 102 outputs awaveform monitoring signal 114 in response to thesensing signals 110. As shown,sensors 108 are coupled topatient 112 to monitor and detect health parameters such as, heart rate, heart beat, blood pressure, specific oxygen, blood sugar, or the like. In one embodiment, thepatient 112 may be a human or other animal such as, a cat or a dog. Thesensors 108 ormonitoring device 102 may be wireless sensors that transmitsensing signals 110 through a wireless network. As shown, themonitoring signal 114 is received bymonitoring station 104 in order to detect alarms and classify alarm patterns and store them in analarm pattern database 116.Database 116 may incorporate a chronological alarm log file containing all alarm times and types during one or more monitored sessions. -
Monitoring station 104 includes an on-line monitoring module 119 and an on-line learning module 120. The on-line monitoring module 119 monitors a patient's physiological status and alerts monitoring personnel when an alarm pattern of significance has been detected via a real-time pattern display 111 that is used by monitoring personnel to view alarm sequence of thepatient 112. In one embodiment, the real-time pattern display 111 may be included in monitoringdevice 102 or inmonitoring station 104. In one embodiment,monitoring station 104 alerts a user, such as a physician, nurse, or other qualified medical personnel when an alarm or alarm pattern fromdatabase 116 is recognized in themonitoring signal 114. The alert may be displayed to the user on the real-time pattern display 111. In addition,monitoring station 104 or real-time pattern display 111 may generate an audible alert.Monitoring station 104, with real-time display 111 may also generate a table or graph showing recorded alarm patterns of interest for thepatient 112 for a user on real-time pattern display 111. In this manner, a physician or other medical practitioner may reviewpatient 112 for a particular period. In one embodiment, themonitoring signal 114 may be multiple signals. In another embodiment, themonitoring station 104 may be coupled to receive amonitoring signal 114 from multiple patients. In one embodiment, themonitoring station 104 may be located in a hospital, clinic, or other medical facility and/or location where monitoring personnel may monitorpatient 112 such as a monitoring facility. - As shown in
FIG. 1 , theremote storage facility 106 is connected to themonitoring station 104 through acommunications link 121 and is configured to communicate with, receive, and store the detected alarm patterns identified by themonitoring station 104. More specifically, theremote storage facility 106 includesalarm pattern database 116 and analarm pattern library 117, which may be accessed in real-time to compare alarm patterns in themonitoring signal 114 with stored alarm patterns inalarm pattern library 117. Additionally, themonitoring station 104 includes an off-line learning module 118 that recognizes and stores alarm sequences while processing electrical medical records to further increase and/or diagnose more physiological states. -
FIG. 2 is aflowchart 200 of an off-line method for discovering and classifying patient alarm patterns according to an embodiment of the present invention. In one example, theflowchart 200 may be implemented in off-line learning module 118 ofFIG. 1 . Inblock 210, a pattern discovery algorithm is implemented by generating a suffix tree data structure to find alarm patterns from multiple patients. More specifically, the suffix tree data structure is used in the pattern discovery algorithm to identify and store new alarm patterns in alarm sequences, in which many alarm patterns may go undetected using traditional alarm pattern detection methods. -
FIG. 5 illustrates an example suffixtree data structure 500 according to the teachings of the present invention. In the suffixtree data structure 500,several alarm types 502 of interest are considered for detecting alarm patterns in an alarm sequence. More specifically the alarm types 502 considered are, a low heart rate event that is represented by a letter ‘L’, an asystole event that is represented by the letter ‘A’, and a tachycardia event that is represented by the symbol ‘T.’ - The suffix
tree data structure 500 includes acentral node 504, a plurality of first stems 508, 510, and 512, a plurality offirst leaves second leaves first leaf 508 is generated when an alarm pattern ‘TL’ is first recognized from an alarm sequence. Another example,second leaf 532 is generated when an alarm pattern ‘TLTLA’ is first recognized. By using a suffix tree data structure any distinct alarm pattern that occurs in the alarm sequence will generate a stem, also referred to as a pathway, to a new leaf specific to that alarm pattern. If a substring of an alarm pattern is common to another alarm pattern, then a stem will be generated from that leaf to another leaf. For example, an alarm pattern ‘TL’ represented byleaf 516 is a substring that is common to the alarm pattern ‘TLTLA’ represented bysecond leaf 536. Since ‘TL’ is common to the alarm pattern TLA,second stem 528 is generated fromfirst leaf 516 to generatesecond leaf 536. If an alarm pattern has already been identified by the suffixtree data structure 500, the number of occurrences for the alarm pattern type corresponding to a patient history will be accounted for with an incidence vector, which is discussed in further detail below. In an alternate embodiment, the repeated occurrences of alarm patterns may be accounted for by weighting the nodes of the suffixtree data structure 500. For example, if pattern type ‘LTLA’ occurs five times thensecond leaf 532 and pattern type ‘LA’ occurs three times, thenfirst leaf 518 that is representative of ‘L’ will be weighted more thansecond leaf first leaf 518 is weighted value equal to the sum of the weighted value ofsecond leafs - One of the realized benefits of the suffix
tree data structure 500, is to find common alarms from a pool of patients with similar histories, also referred to as electric patient medical records (EMR). According to an embodiment of the present invention, a longest common substring may be detected with a suffixtree data structure 500 in order to identify common alarm patterns. More specifically, a longest common substring is defined as the longest string that is a substring of two or more strings. For example, the longest common substring in suffixtree data structure 500 is ‘TL’ which corresponds withfirst stem 508 andfirst node 516. In one embodiment, more than one longest common substring may be determined in a suffix tree data structure. - In one embodiment, a least common substring is determined to determine the alarm pattern or patterns that are the shortest that are common to all patients in a class (i.e., disease category or medical condition). In one example, this may be used to distinguish a rare medical event and/or condition. In one embodiment, a longest common subsequence is determined. More specifically, a longest common subsequence is defined as a longest sequence such as a subsequence of all sequences in a set of sequences. In one example, the longest common subsequence may be set to a maximum and/or a minimum length that is to be identified.
- Referring back to
flowchart 200 inFIG. 2 , inblock 220, the alarm patterns are screened for relevant alarms. More specifically, the alarm patterns may be screened, but not limited to, the longest common substring, the least common substring and the longest common subsequence. This allows certain alarm events that may be irrelevant such as sensor failures, patient movement, or independent alarm events that have no significance, to be excluded from the alarm patterns of interest. - In
block 230, multiple incidence vectors of alarm patterns, each corresponding to a patient recording session, are determined. In one example, each incidence vector determines the relative frequency of occurrence of an alarm pattern type with respect to all the alarm pattern type that occur in the alarm sequence of a particular patient or patient class. As shown inFIG. 6 , a series ofincidence vectors 602 may be calculated fromoccurrence values 604 inalarm pattern library 116. Eachoccurrence value 604 is representative of a ratio of the number of occurrences of an alarm pattern ‘m’ over the total number of alarm pattern occurrences for patient ‘n,’ where ‘n’ is representative of a patient and ‘m’ is representative of an alarm pattern or alarm pattern type. For example,occurrence value 606 is representative of a ratio of the number of occurrences of an alarm pattern ‘2’ over the total number of alarm pattern occurrences for patient ‘1’. - Still referring to
FIG. 6 , in thealarm pattern library 116, eachrow 608 of occurrence values 604 corresponds to arespective incidence vector 602 and eachcolumn 610 of occurrence values 604 corresponds to a specific alarm pattern. Multiple occurrence values 604 make up theincidence vector 602. Eachincidence vector 602 establishes a relative incidence of alarm pattern type with respect to the corresponding alarm sequence during an entire patient monitoring session. - Referring back to
flowchart 200 inFIG. 2 , inblock 230,multiple incidence vectors 602 are determined. In one example,flow chart 200 may be implemented in off-line learning module 118. Inblock 240, a clustering algorithm is used to group incidence vectors representative of alarm sequences together. More specifically, clustering is the classification of objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters) such that the data in each subset share some common trait (in this case common pattern incidence rates). Clustering is beneficial for scalability, defining arbitrary boundaries of a group, ability to deal with noise or outliers, insensitivity to order of input, and high dimensionality (number of patients). In the preferred embodiment, a K-means clustering algorithm is used which is known to one skilled in the art. In other embodiments, other clustering algorithms such as, but not limited to, hierarchical, Fuzzy-C means, or “mixture-of-Gaussians” clustering algorithms may be used. Inblock 250, designing a classifier for clusters of incidence vectors is accomplished using a support vector machine (SVM) algorithm. More specifically, the SVM algorithm will associate alarm pattern statistics with, a particular cluster associated with disease, type of medical condition, and/or general state of health, which is a method of unsupervised learning. For example, the International Classification of Diseases (ICD) includes a taxonomy of cardiac and cardiac-relate diseases and may be used to classify existing electric medical records. In another embodiment, a supervised learning process may classify the incidence vectors. For example, once the clusters have been developed, one or more qualified medical personnel, such as a physician(s), may asses the incidence vectors of alarm sequences to classify each cluster. One benefit of using the SVM algorithm is to efficiently classify non-normally distributed clusters with multi-dimensional data, the dimensions being the number of patterns that are to be classified. -
FIG. 3 is anexample flowchart 300 of an on-line method for detecting and classifying new alarm patterns in real-time according to another embodiment of the present invention. In one example,flow chart 300 may be implemented in on-line learning module 120 and off-line learning module 118. Inblock 310, a suffix tree data structure is developed in real-time as alarm patterns are detected in an alarm sequence. Inblock 320, the suffix tree data structure is compared with analarm pattern library 117 using a comparing algorithm to detect new alarm patterns. If a new alarm pattern is detected 332 inblock 330, then the new alarm pattern type will be updated atblock 340 into thealarm pattern library 117. Inblock 345, the off-line learning process will run when a substantial amount of new alarm patterns have been recognized to update the suffix tree data structure. In one embodiment, block 345 may be executed after every new alarm pattern type is detected. In another embodiment, block 345 may be executed after a threshold number of new alarm pattern types have been detected and stored inalarm pattern library 117. If a new alarm pattern is not detected 346 inblock 330 or after thealarm pattern library 117 is updated inblock 340, the incidence vector will be classified if patient's medical status or other alarm-related condition is known inblock 350. More specifically, the medical status may include types of diseases, medical status of the patient, or the like. If a patient's pattern incidence vector is not recognized, it is stored for future off-line processing, and the patient is classified as “other”, i.e., not similar to any previously known alarm patterns. -
FIG. 4 is aflowchart 400 of a method for real-time alarm pattern detection and alerting of monitoring personnel according to an embodiment of the present invention. In one example,flow chart 400 may be implemented in on-line monitoring module 119. Inblock 410, an alarm sequence is received. In one example, alarm sequences may be received concurrently from multiple individual monitoring signals 114 of a patient. In another example, themonitoring signal 114 may be an interdigitated alarm signal that represents alarm events of different types and possibly for multiple physiological parameters. Inblock 420, as the alarm sequence progresses, a suffix tree data structure is developed in real-time to identify alarm patterns. Inblock 430, an incidence vector is built up in real-time in response to the suffix tree data structure. Inblock 440, a support vector machine algorithm is used to group the incidence vector with a cluster when sufficient alarm sequence data has been accumulated. Inblock 450, an alert is raised to monitoring personnel to indicate a particular classification of the patient. In one embodiment, if certain critical alarm patterns are detected early in the alarm sequence, it may be possible to classify the alarm sequence with a particular “critical” cluster/group before the end of the alarm sequence. In another embodiment, if certain critical alarm patterns are discovered in an alarm sequence, it may be possible to eliminate group/clusters that are classified with non-significant phenomena, such as, ‘normal behavior,’ ‘stable,’ and/or ‘recovering.’ Early classification before the alarm sequence is complete may provide prognostic value in critical conditions. - A technical contribution for the disclosed method and apparatus is that is provides for a computer implemented method for discovery, classification and detection of alarm patterns for electrophysiological monitoring systems.
- Therefore, according to an embodiment of the present invention, an electrophysiological monitoring system including a plurality of sensors configured to detect one or more health parameters of a patient and a monitoring device configured to receive a plurality of sensing signals from the sensors and output a monitoring signal representative of an alarm sequence, wherein the alarm sequence comprises a set of alarm events identified in the sensing signals. The system also includes an on-line monitoring module configured to generate a suffix tree data structure in response to the monitoring signal to identify alarm patterns from the set of alarm events and classify the alarm sequence in response to the occurrences of alarm patterns in the alarm sequence. The on-line monitoring module is further configured to alert monitoring personnel of an alarm condition after processing the alarm sequence in real-time.
- According to another embodiment of the present invention, a method for electrophysiological monitoring includes receiving a monitoring signal derived from a patient wherein the monitoring signal provides an alarm sequence, developing a suffix substring data structure to identify a plurality of alarm patterns from the alarm sequence, and generating an incidence vector that determines a relative incidence of each type of alarm pattern with respect to the plurality of alarm patterns in the alarm sequence. The method further includes grouping the incidence vector in a cluster using a clustering algorithm, classifying one or more patients according to their pattern incident rates which are obtained by extraction of alarm patterns and storing the classification of the incidence vector in a database.
- According to yet another embodiment of the present invention, a method for electrophysiological monitoring includes receiving a monitoring signal from a patient wherein the monitoring signal provides an alarm sequence, wherein the alarm sequence includes at least one alarm pattern. The method also includes developing a suffix substring data structure in real-time to identify alarm patterns of medical interest contained in the alarm sequence and generating an incidence vector in response to the suffix substring data structure. The alarm sequence is classified to a classification cluster using a classification algorithm based on the incidence vector and monitoring personnel is alerted to indicate a condition corresponding to the cluster after processing the alarm sequence in real-time.
- The present invention has been described in terms of the preferred embodiment, and it is recognized that equivalents, alternatives, and modifications, aside from those expressly stated, are possible and within the scope of the appending claims.
Claims (21)
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/749,400 US7679504B2 (en) | 2007-05-16 | 2007-05-16 | System and method of discovering, detecting and classifying alarm patterns for electrophysiological monitoring systems |
GB0808744A GB2449347A (en) | 2007-05-16 | 2008-05-14 | Detecting and classifying alarm patterns for electrophysiological monitoring systems |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/749,400 US7679504B2 (en) | 2007-05-16 | 2007-05-16 | System and method of discovering, detecting and classifying alarm patterns for electrophysiological monitoring systems |
Publications (2)
Publication Number | Publication Date |
---|---|
US20080284582A1 true US20080284582A1 (en) | 2008-11-20 |
US7679504B2 US7679504B2 (en) | 2010-03-16 |
Family
ID=39571326
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US11/749,400 Active 2028-06-03 US7679504B2 (en) | 2007-05-16 | 2007-05-16 | System and method of discovering, detecting and classifying alarm patterns for electrophysiological monitoring systems |
Country Status (2)
Country | Link |
---|---|
US (1) | US7679504B2 (en) |
GB (1) | GB2449347A (en) |
Cited By (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20100261982A1 (en) * | 2007-12-06 | 2010-10-14 | Norbert Noury | Method and apparatus for detecting a critical situation of a subject |
US20120041277A1 (en) * | 2010-08-12 | 2012-02-16 | International Business Machines Corporation | System and method for predicting near-term patient trajectories |
US8589187B2 (en) | 2010-12-28 | 2013-11-19 | Microsoft Corporation | Automated clustering for patient disposition |
CN104915360A (en) * | 2014-03-14 | 2015-09-16 | 通用电气公司 | Medical monitoring system and alarm song reducing method applied to medical monitoring system |
US20150308920A1 (en) * | 2014-04-24 | 2015-10-29 | Honeywell International Inc. | Adaptive baseline damage detection system and method |
US9275368B1 (en) * | 2012-09-25 | 2016-03-01 | Amazon Technologies, Inc. | Annotation mapping |
WO2016103197A1 (en) * | 2014-12-23 | 2016-06-30 | Performance Lab Technologies Limited | Classifying multiple activity events |
US20160220197A1 (en) * | 2015-01-29 | 2016-08-04 | Börje Rantala | Alarm generation method and artefact rejection for patient monitor |
US20190188929A1 (en) * | 2017-12-18 | 2019-06-20 | Infineon Technologies Ag | Method and apparatus for processing alarm signals |
US11037070B2 (en) * | 2015-04-29 | 2021-06-15 | Siemens Healthcare Gmbh | Diagnostic test planning using machine learning techniques |
CN113164071A (en) * | 2018-08-07 | 2021-07-23 | 金达中华有限公司 | Health map for navigating health space |
US20210343410A1 (en) * | 2020-05-02 | 2021-11-04 | Petuum Inc. | Method to the automatic International Classification of Diseases (ICD) coding for clinical records |
Families Citing this family (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20100211192A1 (en) * | 2009-02-17 | 2010-08-19 | Honeywell International Inc. | Apparatus and method for automated analysis of alarm data to support alarm rationalization |
US9355477B2 (en) | 2011-06-28 | 2016-05-31 | Honeywell International Inc. | Historical alarm analysis apparatus and method |
WO2013056180A1 (en) | 2011-10-13 | 2013-04-18 | The Regents Of The University Of California | System and methods for generating predictive combinations of hospital monitor alarms |
US9526437B2 (en) | 2012-11-21 | 2016-12-27 | i4c Innovations Inc. | Animal health and wellness monitoring using UWB radar |
US10149617B2 (en) | 2013-03-15 | 2018-12-11 | i4c Innovations Inc. | Multiple sensors for monitoring health and wellness of an animal |
US9760546B2 (en) * | 2013-05-24 | 2017-09-12 | Xerox Corporation | Identifying repeat subsequences by left and right contexts |
US9268749B2 (en) * | 2013-10-07 | 2016-02-23 | Xerox Corporation | Incremental computation of repeats |
WO2015062896A1 (en) | 2013-11-01 | 2015-05-07 | Koninklijke Philips N.V. | Apparatus and method for acoustic alarm detection and validation |
US10586172B2 (en) * | 2016-06-13 | 2020-03-10 | General Electric Company | Method and system of alarm rationalization in an industrial control system |
US10733533B2 (en) * | 2017-03-07 | 2020-08-04 | General Electric Company | Apparatus and method for screening data for kernel regression model building |
US10635096B2 (en) | 2017-05-05 | 2020-04-28 | Honeywell International Inc. | Methods for analytics-driven alarm rationalization, assessment of operator response, and incident diagnosis and related systems |
US10747207B2 (en) | 2018-06-15 | 2020-08-18 | Honeywell International Inc. | System and method for accurate automatic determination of “alarm-operator action” linkage for operator assessment and alarm guidance using custom graphics and control charts |
CN111953541B (en) * | 2020-08-10 | 2023-12-05 | 腾讯科技(深圳)有限公司 | Alarm information processing method, device, computer equipment and storage medium |
WO2023023366A1 (en) | 2021-08-19 | 2023-02-23 | Rehrig Pacific Company | Imaging system with unsupervised learning |
Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4977390A (en) * | 1989-10-19 | 1990-12-11 | Niagara Mohawk Power Corporation | Real time method for processing alaarms generated within a predetermined system |
US6024699A (en) * | 1998-03-13 | 2000-02-15 | Healthware Corporation | Systems, methods and computer program products for monitoring, diagnosing and treating medical conditions of remotely located patients |
US20020055790A1 (en) * | 2000-11-07 | 2002-05-09 | Havekost Robert B. | Enhanced device alarms in a process control system |
US6690274B1 (en) * | 1998-05-01 | 2004-02-10 | Invensys Systems, Inc. | Alarm analysis tools method and apparatus |
US20040068332A1 (en) * | 2001-02-20 | 2004-04-08 | Irad Ben-Gal | Stochastic modeling of spatial distributed sequences |
US6804656B1 (en) * | 1999-06-23 | 2004-10-12 | Visicu, Inc. | System and method for providing continuous, expert network critical care services from a remote location(s) |
US20050108384A1 (en) * | 2003-10-23 | 2005-05-19 | Lambert John R. | Analysis of message sequences |
US6988969B2 (en) * | 2002-04-24 | 2006-01-24 | Nike, Inc. | Game ball with bridged panels |
US20060192667A1 (en) * | 2002-01-24 | 2006-08-31 | Ammar Al-Ali | Arrhythmia alarm processor |
US20070032705A1 (en) * | 2003-10-10 | 2007-02-08 | Koninklijke Philips Electronics N.V. | System and method to estimate signal artifacts |
Family Cites Families (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5047930A (en) * | 1987-06-26 | 1991-09-10 | Nicolet Instrument Corporation | Method and system for analysis of long term physiological polygraphic recordings |
GB9212133D0 (en) * | 1992-06-09 | 1992-07-22 | Polytechnic South West | Medical signal analyzer |
ES2362414T3 (en) | 2000-05-19 | 2011-07-05 | Welch Allyn Protocol Inc | PATIENT MONITORING SYSTEM. |
JP3672242B2 (en) * | 2001-01-11 | 2005-07-20 | インターナショナル・ビジネス・マシーンズ・コーポレーション | PATTERN SEARCH METHOD, PATTERN SEARCH DEVICE, COMPUTER PROGRAM, AND STORAGE MEDIUM |
GB0118728D0 (en) * | 2001-07-31 | 2001-09-26 | Univ Belfast | Monitoring device |
US7440461B2 (en) * | 2003-12-23 | 2008-10-21 | Intel Corporation | Methods and apparatus for detecting patterns in a data stream |
EP2609855B1 (en) * | 2005-02-22 | 2023-11-15 | Admetsys Corporation | Balanced physiological monitoring and treatment system |
-
2007
- 2007-05-16 US US11/749,400 patent/US7679504B2/en active Active
-
2008
- 2008-05-14 GB GB0808744A patent/GB2449347A/en not_active Withdrawn
Patent Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4977390A (en) * | 1989-10-19 | 1990-12-11 | Niagara Mohawk Power Corporation | Real time method for processing alaarms generated within a predetermined system |
US6024699A (en) * | 1998-03-13 | 2000-02-15 | Healthware Corporation | Systems, methods and computer program products for monitoring, diagnosing and treating medical conditions of remotely located patients |
US6690274B1 (en) * | 1998-05-01 | 2004-02-10 | Invensys Systems, Inc. | Alarm analysis tools method and apparatus |
US6804656B1 (en) * | 1999-06-23 | 2004-10-12 | Visicu, Inc. | System and method for providing continuous, expert network critical care services from a remote location(s) |
US20020055790A1 (en) * | 2000-11-07 | 2002-05-09 | Havekost Robert B. | Enhanced device alarms in a process control system |
US20040068332A1 (en) * | 2001-02-20 | 2004-04-08 | Irad Ben-Gal | Stochastic modeling of spatial distributed sequences |
US20060192667A1 (en) * | 2002-01-24 | 2006-08-31 | Ammar Al-Ali | Arrhythmia alarm processor |
US6988969B2 (en) * | 2002-04-24 | 2006-01-24 | Nike, Inc. | Game ball with bridged panels |
US20070032705A1 (en) * | 2003-10-10 | 2007-02-08 | Koninklijke Philips Electronics N.V. | System and method to estimate signal artifacts |
US20050108384A1 (en) * | 2003-10-23 | 2005-05-19 | Lambert John R. | Analysis of message sequences |
Cited By (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20100261982A1 (en) * | 2007-12-06 | 2010-10-14 | Norbert Noury | Method and apparatus for detecting a critical situation of a subject |
US20120041277A1 (en) * | 2010-08-12 | 2012-02-16 | International Business Machines Corporation | System and method for predicting near-term patient trajectories |
US8589187B2 (en) | 2010-12-28 | 2013-11-19 | Microsoft Corporation | Automated clustering for patient disposition |
US9275368B1 (en) * | 2012-09-25 | 2016-03-01 | Amazon Technologies, Inc. | Annotation mapping |
CN104915360A (en) * | 2014-03-14 | 2015-09-16 | 通用电气公司 | Medical monitoring system and alarm song reducing method applied to medical monitoring system |
US20150308920A1 (en) * | 2014-04-24 | 2015-10-29 | Honeywell International Inc. | Adaptive baseline damage detection system and method |
WO2016103197A1 (en) * | 2014-12-23 | 2016-06-30 | Performance Lab Technologies Limited | Classifying multiple activity events |
US20160220197A1 (en) * | 2015-01-29 | 2016-08-04 | Börje Rantala | Alarm generation method and artefact rejection for patient monitor |
US11037070B2 (en) * | 2015-04-29 | 2021-06-15 | Siemens Healthcare Gmbh | Diagnostic test planning using machine learning techniques |
US20190188929A1 (en) * | 2017-12-18 | 2019-06-20 | Infineon Technologies Ag | Method and apparatus for processing alarm signals |
US10580233B2 (en) * | 2017-12-18 | 2020-03-03 | Infineon Technologies Ag | Method and apparatus for processing alarm signals |
CN113164071A (en) * | 2018-08-07 | 2021-07-23 | 金达中华有限公司 | Health map for navigating health space |
US20210343410A1 (en) * | 2020-05-02 | 2021-11-04 | Petuum Inc. | Method to the automatic International Classification of Diseases (ICD) coding for clinical records |
Also Published As
Publication number | Publication date |
---|---|
GB0808744D0 (en) | 2008-06-18 |
GB2449347A (en) | 2008-11-19 |
US7679504B2 (en) | 2010-03-16 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US7679504B2 (en) | System and method of discovering, detecting and classifying alarm patterns for electrophysiological monitoring systems | |
US11727279B2 (en) | Method and apparatus for performing anomaly detection using neural network | |
Apiletti et al. | Real-time analysis of physiological data to support medical applications | |
Jadhav et al. | Artificial neural network models based cardiac arrhythmia disease diagnosis from ECG signal data | |
JP7070255B2 (en) | Abnormality discrimination program, abnormality discrimination method and abnormality discrimination device | |
CN108604465B (en) | Prediction of Acute Respiratory Disease Syndrome (ARDS) based on patient physiological responses | |
Tsien et al. | Multiple signal integration by decision tree induction to detect artifacts in the neonatal intensive care unit | |
Hackmann et al. | Toward a two-tier clinical warning system for hospitalized patients | |
Ge et al. | Multi-label correlation guided feature fusion network for abnormal ECG diagnosis | |
Bai et al. | Integrating monitor alarms with laboratory test results to enhance patient deterioration prediction | |
EP3191988A1 (en) | Method and apparatus for disease detection | |
Kumar et al. | An Approach Using Fuzzy Sets and Boosting Techniques to Predict Liver Disease. | |
CN104823195A (en) | Method and system to reduce nuisance alarm load in clinical setting | |
Merone et al. | A decision support system for tele-monitoring COPD-related worrisome events | |
Kristinsson et al. | Prediction of serious outcomes based on continuous vital sign monitoring of high-risk patients | |
KR102421172B1 (en) | Smart Healthcare Monitoring System and Method for Heart Disease Prediction Based On Ensemble Deep Learning and Feature Fusion | |
Kangwanariyakul et al. | Data mining of magnetocardiograms for prediction of ischemic heart disease | |
Pandey et al. | ECG arrhythmia detection with machine learning algorithms | |
Ahammad | Risk factor identification for stroke prognosis using machine-learning algorithms | |
Tallapragada et al. | Improved atrial fibrillation detection using CNN-LSTM | |
Lehman et al. | Similarity-based searching in multi-parameter time series databases | |
Pal et al. | Two-stage classifier for resource constrained on-board cardiac arrhythmia detection | |
US20240221939A1 (en) | System and method for automated discovery of time series trends without imputation | |
Anil et al. | Prediction of Chronic Kidney Disease Using Various Machine Learning Algorithms | |
Wong et al. | Probabilistic detection of vital sign abnormality with Gaussian process regression |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: GENERAL ELECTRIC COMPANY, NEW YORK Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:WANG, XI;JOHNSON, TIMOTHY L.;TREACY, STEPHEN T.;REEL/FRAME:019370/0733;SIGNING DATES FROM 20070515 TO 20070601 Owner name: GENERAL ELECTRIC COMPANY,NEW YORK Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:WANG, XI;JOHNSON, TIMOTHY L.;TREACY, STEPHEN T.;SIGNING DATES FROM 20070515 TO 20070601;REEL/FRAME:019370/0733 |
|
FEPP | Fee payment procedure |
Free format text: PAYOR NUMBER ASSIGNED (ORIGINAL EVENT CODE: ASPN); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY |
|
STCF | Information on status: patent grant |
Free format text: PATENTED CASE |
|
FPAY | Fee payment |
Year of fee payment: 4 |
|
MAFP | Maintenance fee payment |
Free format text: PAYMENT OF MAINTENANCE FEE, 8TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1552) Year of fee payment: 8 |
|
MAFP | Maintenance fee payment |
Free format text: PAYMENT OF MAINTENANCE FEE, 12TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1553); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY Year of fee payment: 12 |